Kingldore/dude-vs

DUD-E Docked Poses (gnina virtual screening)

The docked poses from dude_vs.tgz, produced by the Koes lab at the University of Pittsburgh for Virtual Screening with Gnina 1.0.

The original is one 14.7 GB archive that must be read start to finish. Here it is split by target into dude-vs/, one archive per DUD-E target, so you can download only the targets you need:

hf download Kingldore/dude-vs dude-vs/cdk2.tar.gz --repo-type dataset
tar -xzf dude-vs/cdk2.tar.gz

102 targets, 4,363 files, 14.7 GB in total. The contents are unchanged from the original; only the packaging differs.

Score table

dude_gnina_scores.parquet summarises every docked pose into one row per (target, compound_id) — 1,422,897 rows, 22,784 actives and 1,400,113 decoys.

column	meaning
target	DUD-E target name
compound_id	ZINC or CHEMBL identifier
label	1 = active, 0 = decoy
n_poses	poses the statistics were computed over
gnina_best_*	value at the pose with the highest CNNscore
gnina_mean_*, gnina_std_*	mean and population standard deviation over all poses

The four score families are minimized_affinity, cnn_score, cnn_affinity, and cnn_vs (CNNscore x CNNaffinity, computed per pose then aggregated).

minimized_affinity is the empirical docking score, from the column the summary files label Vina. DUD-E was docked with default smina arguments (--seed 0 --autobox_add 4 --num_modes 9), so this is the Vina scoring function, and num_modes 9 is why most ligands have nine poses. The pose files are named *_docked_vinardo.sdf.gz because Vinardo rescoring was also carried out; those scores are in a separate vinardo.summary and are not included here.

All four gnina_best_* values come from the same pose — the one with the highest CNNscore — rather than each column being maximised independently.

Most ligands have 9 poses. A few are docked more than once against a target; identical pose rows are dropped and the rest pooled, so n_poses can exceed 9.

Original download: https://bits.csb.pitt.edu/files/gninavs/dude_vs.tgz

Citation

Please cite both the method and the underlying benchmark.

gnina virtual screening — the source of these docked poses:

@article{sunseri2021virtual,
  title   = {Virtual Screening with Gnina 1.0},
  author  = {Sunseri, Jocelyn and Koes, David Ryan},
  journal = {Molecules},
  volume  = {26},
  number  = {23},
  pages   = {7369},
  year    = {2021},
  doi     = {10.3390/molecules26237369}
}

DUD-E — the benchmark set that was docked:

@article{mysinger2012directory,
  title   = {Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and
             Decoys for Better Benchmarking},
  author  = {Mysinger, Michael M. and Carchia, Michael and Irwin, John J. and
             Shoichet, Brian K.},
  journal = {Journal of Medicinal Chemistry},
  volume  = {55},
  number  = {14},
  pages   = {6582--6594},
  year    = {2012},
  doi     = {10.1021/jm300687e}
}

Links

• Paper: https://doi.org/10.3390/molecules26237369
• DUD-E paper: https://doi.org/10.1021/jm300687e
• gnina software: https://github.com/gnina/gnina
• DUD-E: https://dude.docking.org/
• Koes lab file index: https://bits.csb.pitt.edu/files/

License

This is a mirror hosted for convenience; the data belongs to its original authors and no additional licence is granted here.